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Recursive partitioning clustering tree algorithm

Authors
Kang, Ji HoonPark, Chan HeeKim, Seoung Bum
Issue Date
5월-2016
Publisher
SPRINGER
Keywords
Unsupervised learning; Clustering algorithm; Recursive binary partitioning; Silhouette statistic
Citation
PATTERN ANALYSIS AND APPLICATIONS, v.19, no.2, pp.355 - 367
Indexed
SCIE
SCOPUS
Journal Title
PATTERN ANALYSIS AND APPLICATIONS
Volume
19
Number
2
Start Page
355
End Page
367
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/88798
DOI
10.1007/s10044-014-0399-1
ISSN
1433-7541
Abstract
Clustering analysis elicits the natural groupings of a dataset without requiring information about the sample class and has been widely used in various fields. Although numerous clustering algorithms have been proposed and proven to perform reasonably well, no consensus exists about which one performs best in real situations. In this study, we propose a nonparametric clustering method based on recursive binary partitioning that was implemented in a classification and regression tree model. The proposed clustering algorithm has two key advantages: (1) users do not have to specify any parameters before running it; (2) the final clustering result is represented by a set of if-then rules, thereby facilitating analysis of the clustering results. Experiments with the simulations and real datasets demonstrate the effectiveness and usefulness of the proposed algorithm.
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